We are drowning in information,

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Data Mining

• We are drowning in information,
• but starving for knowledge
• John Naisbett

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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

3
Evolution of Database Technology

• 1960s
• Data collection, database creation, IMS and
  network DBMS
• 1970s
• Relational data model, relational DBMS
  implementation
• 1980s
• RDBMS, advanced data models (extended-relational,
  OO, deductive, etc.) and application-oriented
  DBMS (spatial, scientific, engineering, etc.)
• 1990sToday
• Data mining and data warehousing, multimedia
  databases, and web databases

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Motivation Necessity is the Mother of
Invention

• Explosive growth in our capabilities to generate
  and collect data
• Introduction of barcodes for almost all
  commercial products
• Computerization of many business and government
  transactions
• Advances in data storage technology (faster,
  cheaper, higher capacity storage devices
• Better DBMS Data Warehousing technology

Mountains of stored data
Knowledge Discovery in Databases
(KDD) first conference on KDD in 1991
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Names used historically...

• Knowledge Discovery in Databases (KDD)
• Data Mining
• Knowledge Extraction
• Information Discovery
• Information Harvesting
• Data Archaeology
• Data Pattern Processing

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Names used today...

• Knowledge Discovery (KDD) refers to
• Overall process of discovering useful knowledge
  from data
• used by artificial intelligence and machine
  learning researchers
• Data Mining refers to
• Application of algorithms for extracting
  patterns from data
• used by statisticians, data analysts and MIS
  community

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Knowledge Discovery in Databases (KDD)
KDD is non-trivial process of identifying valid,
novel, potentially useful, and ultimately
understandable patterns in data Data Mining is a
step in KDD process consisting of particular data
mining algorithms that under some acceptable
computational efficiency limitations, produces
particular enumaration of patterns
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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

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Potential Data Mining Applications

• Database analysis and decision support
• Market analysis and management
• target marketing, customer relation management,
  market basket analysis, cross selling, market
  segmentation
• Risk analysis and management
• Forecasting, customer retention, improved
  underwriting, quality control, competitive
  analysis
• Fraud detection and management
• Other Applications
• Text mining (news group, email, documents) and
  Web analysis.
• Intelligent query answering

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Market Analysis and Management (1)

• Where are the data sources for analysis?
• Credit card transactions, loyalty cards, discount
  coupons, customer complaint calls, plus (public)
  lifestyle studies
• Target marketing
• Find clusters of model customers who share the
  same characteristics interest, income level,
  spending habits, etc.
• Determine customer purchasing patterns over time
• Conversion of single to a joint bank account
  marriage, etc.
• Cross-market analysis
• Associations/co-relations between product sales
• Prediction based on the association information

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Market Analysis and Management (2)

• Customer profiling
• data mining can tell you what types of customers
  buy what products (clustering or classification)
• Identifying customer requirements
• identifying the best products for different
  customers
• use prediction to find what factors will attract
  new customers
• Provides summary information
• various multidimensional summary reports
• statistical summary information (data central
  tendency and variation)

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Corporate Analysis and Risk Management

• Finance planning and asset evaluation
• cash flow analysis and prediction
• contingent claim analysis to evaluate assets
• cross-sectional and time series analysis
  (financial-ratio, trend analysis, etc.)
• Resource planning
• summarize and compare the resources and spending
• Competition
• monitor competitors and market directions
• group customers into classes and a class-based
  pricing procedure
• set pricing strategy in a highly competitive
  market

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Fraud Detection and Management (1)

• Applications
• widely used in health care, retail, credit card
  services, telecommunications (phone card fraud),
  intrusion detection, etc.
• Approach
• use historical data to build models of fraudulent
  behavior and use data mining to help identify
  similar instances
• Examples
• auto insurance detect a group of people who
  stage accidents to collect on insurance
• money laundering detect suspicious money
  transactions (US Treasury's Financial Crimes
  Enforcement Network)
• medical insurance detect professional patients
  and ring of doctors and ring of references

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Fraud Detection and Management (2)

• Detecting inappropriate medical treatment
• Australian Health Insurance Commission identifies
  that in many cases blanket screening tests were
  requested (save Australian 1m/yr).
• Detecting telephone fraud
• Telephone call model destination of the call,
  duration, time of day or week. Analyze patterns
  that deviate from an expected norm.
• British Telecom identified discrete groups of
  callers with frequent intra-group calls,
  especially mobile phones, and broke a
  multimillion dollar fraud.
• Retail
• Analysts estimate that 38 of retail shrink is
  due to dishonest employees.
• Computer Security
• Analysis of user usage profiles, system
  utilization patterns

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Other Applications

• Sports
• IBM Advanced Scout analyzed NBA game statistics
  (shots blocked, assists, and fouls) to gain
  competitive advantage for New York Knicks and
  Miami Heat
• Astronomy
• JPL and the Palomar Observatory discovered 22
  quasars with the help of data mining
• Internet Web Surf-Aid
• IBM Surf-Aid applies data mining algorithms to
  Web access logs for market-related pages to
  discover customer preference and behavior pages,
  analyzing effectiveness of Web marketing,
  improving Web site organization, etc.

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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

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Data Mining A KDD Process
Knowledge
Pattern Evaluation

• Data mining the core of knowledge discovery
  process.

Data Mining
Task-relevant Data
Selection
Data Warehouse
Data Cleaning
Data Integration
Databases
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Steps of a KDD Process

• Learning the application domain
• relevant prior knowledge and goals of application
• Creating a target data set data selection
• Data cleaning and preprocessing (may take 60 of
  effort!)
• Data reduction and transformation
• Find useful features, dimensionality/variable
  reduction, invariant representation.
• Choosing functions of data mining
• summarization, classification, regression,
  association, clustering.
• Choosing the mining algorithm(s)
• Data mining search for patterns of interest
• Pattern evaluation and knowledge presentation
• visualization, transformation, removing redundant
  patterns, etc.
• Use of discovered knowledge

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Data Mining and Business Intelligence
Increasing potential to support business decisions
End User
Making Decisions
Business Analyst
Data Presentation
Visualization Techniques
Data Mining
Data Analyst
Information Discovery
Data Exploration
Statistical Analysis, Querying and Reporting
Data Warehouses / Data Marts
OLAP, MDA
DBA
Data Sources
Paper, Files, Information Providers, Database
Systems, OLTP
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Architecture of a Typical Data Mining System
Graphical user interface
Pattern evaluation
Data mining engine
Knowledge-base
Database or data warehouse server
Filtering
Data cleaning data integration
Data Warehouse
Databases
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Data Mining On What Kind of Data?

• Relational databases
• Data warehouses
• Transactional databases
• Advanced DB and information repositories
• Object-oriented and object-relational databases
• Spatial databases
• Time-series data and temporal data
• Text databases and multimedia databases
• Heterogeneous and legacy databases
• WWW

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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

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Data Mining Functionalities (1)

• Concept description Characterization and
  discrimination
• Generalize, summarize, and contrast data
  characteristics, e.g., dry vs. wet regions
• Association (correlation and causality)
• Multi-dimensional vs. single-dimensional
  association
• age(X, 20..29) income(X, 20..29K) à buys(X,
  PC) support 2, confidence 60
• contains(T, computer) à contains(x, software)
  1, 75

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Data Mining Functionalities (2)

• Classification and Prediction
• Finding models (functions) that describe and
  distinguish classes or concepts for future
  prediction
• E.g., classify countries based on climate, or
  classify cars based on gas mileage
• Presentation decision-tree, classification rule,
  neural network
• Prediction Predict some unknown or missing
  numerical values
• Cluster analysis
• Class label is unknown Group data to form new
  classes, e.g., cluster houses to find
  distribution patterns
• Clustering based on the principle maximizing the
  intra-class similarity and minimizing the
  interclass similarity

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Data Mining Functionalities (3)

• Outlier analysis
• Outlier a data object that does not comply with
  the general behavior of the data
• It can be considered as noise or exception but is
  quite useful in fraud detection, rare events
  analysis
• Trend and evolution analysis
• Trend and deviation regression analysis
• Sequential pattern mining, periodicity analysis
• Similarity-based analysis
• Other pattern-directed or statistical analysis

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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

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Data Mining Confluence of Multiple Disciplines
Database Technology
Statistics
Data Mining
Machine Learning
Visualization
Information Science
Other Disciplines
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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

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Data Mining Classification Schemes

• General functionality
• Descriptive data mining
• Predictive data mining
• Different views, different classifications
• Kinds of databases to be mined
• Kinds of knowledge to be discovered
• Kinds of techniques utilized
• Kinds of applications adapted

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Data Mining Classification Schemes

• Databases to be mined
• Relational, transactional, object-oriented,
  object-relational, active, spatial, time-series,
  text, multi-media, heterogeneous, legacy, WWW,
  etc.
• Knowledge to be mined
• Characterization, discrimination, association,
  classification, clustering, trend, deviation and
  outlier analysis, etc.
• Multiple/integrated functions and mining at
  multiple levels
• Techniques utilized
• Database-oriented, data warehouse (OLAP), machine
  learning, statistics, visualization, neural
  network, etc.
• Applications adapted
• Retail, telecommunication, banking, fraud
  analysis, DNA mining, stock market analysis, Web
  mining, Web log analysis, etc.

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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

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Major Issues in Data Mining (1)

• Mining methodology and user interaction
• Mining different kinds of knowledge in databases
• Interactive mining of knowledge at multiple
  levels of abstraction
• Incorporation of background knowledge
• Data mining query languages and ad-hoc data
  mining
• Expression and visualization of data mining
  results
• Handling noise and incomplete data
• Pattern evaluation the interestingness problem
• Performance and scalability
• Efficiency and scalability of data mining
  algorithms
• Parallel, distributed and incremental mining
  methods

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Major Issues in Data Mining (2)

• Issues relating to the diversity of data types
• Handling relational and complex types of data
• Mining information from heterogeneous databases
  and global information systems (WWW)
• Issues related to applications and social impacts
• Application of discovered knowledge
• Domain-specific data mining tools
• Intelligent query answering
• Process control and decision making
• Integration of the discovered knowledge with
  existing knowledge A knowledge fusion problem
• Protection of data security, integrity, and
  privacy

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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

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Selecting a Data Mining Task

• Major data mining functions
• Summary (Characterization)
• Association
• Classification
• Prediction
• Clustering
• Time-Series Analysis

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Mining Characteristic Rules

• Characterization Data generalization/summarizati
  on at high abstraction levels.
• An example query Find a characteristic rule for
  Cities from the database CITYDATA' in
  relevance to location, capita_income, and the
  distribution of count and amount.

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Browsing a Data Cube

• Powerful visualization
• OLAP capabilities
• Interactive manipulation

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Visualization of Data Dispersion Boxplot Analysis
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Mining Association Rules ( Table Form )
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Association Rule in Plane Form
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Association Rule Graph
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Mining Classification Rules
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Prediction Numerical Data
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Prediction Categorical Data
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Data Mining

• Background
• Applications
• Architecture
• Functionalities
• Confluence of Disciplines
• Classification Schemes
• Major Issues
• Graphical Presentation Examples
• Summary
• References

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Summary

• Data mining discovering interesting patterns
  from large amounts of data

• A natural evolution of database technology, in
  great demand, with wide applications

• A KDD process includes data cleaning, data
  integration, data selection, transformation, data
  mining, pattern evaluation, and knowledge
  presentation

• Mining can be performed in a variety of
  information repositories

• Data mining functionalities characterization,
  discrimination, association, classification,
  clustering, outlier and trend analysis, etc.

• Classification of data mining systems can be done
  according to the functionality, database,
  knowledge, technique or application

• Major issues in data mining are methodology, user
  interaction, performance, scalability, data
  types, domain and social impacts

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References

• U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and
  R. Uthurusamy. Advances in Knowledge Discovery
  and Data Mining. AAAI/MIT Press, 1996.
• J. Han and M. Kamber. Data Mining Concepts and
  Techniques. Morgan Kaufmann, 2000.
• T. Imielinski and H. Mannila. A database
  perspective on knowledge discovery.
  Communications of ACM, 3958-64, 1996.
• G. Piatetsky-Shapiro, U. Fayyad, and P. Smith.
  From data mining to knowledge discovery An
  overview. In U.M. Fayyad, et al. (eds.), Advances
  in Knowledge Discovery and Data Mining, 1-35.
  AAAI/MIT Press, 1996.
• G. Piatetsky-Shapiro and W. J. Frawley. Knowledge
  Discovery in Databases. AAAI/MIT Press, 1991.

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Thank you...